Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmac

Beads-on-String Model for Virtual Rectum Surgery Simulation

A beads-on-string model is proposed to handle the deformation and collision of the rectum in virtual surgery simulation. The idea is firstly inspired by the observation of the similarity in shape shared by a rectum with regular bulges and a string of beads. It is beneficial to introduce an additional layer of beads, which provides an interface to map the deformation of centreline to the associated mesh in an elegant manner and a bounding volume approximation in collision handling. Our approach is carefully crafted to achieve high computational efficiency and retain its physical basis. It can be implemented for real time surgery simulation application

Charge Measurement of Cosmic Ray Nuclei with the Plastic Scintillator Detector of DAMPE

One of the main purposes of the DArk Matter Particle Explorer (DAMPE) is to measure the cosmic ray nuclei up to several tens of TeV or beyond, whose origin and propagation remains a hot topic in astrophysics. The Plastic Scintillator Detector (PSD) on top of DAMPE is designed to measure the charges of cosmic ray nuclei from H to Fe and serves as a veto detector for discriminating gamma-rays from charged particles. We propose in this paper a charge reconstruction procedure to optimize the PSD performance in charge measurement. Essentials of our approach, including track finding, alignment of PSD, light attenuation correction, quenching and equalization correction are described detailedly in this paper after a brief description of the structure and operational principle of the PSD. Our results show that the PSD works very well and almost all the elements in cosmic rays from H to Fe are clearly identified in the charge spectrum.Comment: 20 pages, 4 figure

An RBF-based reparameterization method for constrained texture mapping

Texture mapping has long been used in computer graphics to enhance the realism of virtual scenes. However, to match the 3D model feature points with the corresponding pixels in a texture image, surface parameterization must satisfy specific positional constraints. However, despite numerous research efforts, the construction of a mathematically robust, foldover‐free parameterization that is subject to positional constraints continues to be a challenge. In the present paper, this foldover problem is addressed by developing radial basis function (RBF) based reparameterization. Given initial 2D embedding of a 3D surface, the proposed method can reparameterize 2D embedding into a foldover ‐free 2D mesh, satisfying a set of user‐specified constraint points. In addition, this approach is mesh‐free. Therefore, generating smooth texture mapping results is possible without extra smoothing optimization

Gauss-Bonnet solution with a cloud of strings in de Sitter and anti-de Sitter space

In this paper, we present exact spherically symmetric Gauss-Bonnet black hole solutions surrounded by a cloud of strings fluid with cosmological constant in $D>4$ dimensions. Both charged and uncharged cases are considered. We focus on the de Sitter solutions in the main text and leave the Anti-de Sitter solutions in the appendix. We analyze the features of thermodynamic properties of the black hole solutions. The mass, Hawking temperature as well as thermal stability and the phase transitions are discussed. Moreover, the equation of state and critical phenomena associated with these solutions are also explored.Comment: 16 pages, 7 figure

Review on Wave Energy Technologies and Power Equipment for Tropical Reefs

As a promising renewable resource to replace part of the energy supply, the wave energy is having more and more interest worldwide. This paper presents a comprehensive analysis of different wave energy technologies in order to identify more promising methods for power supply to tropical reefs. It starts with summarizing the characteristics of tropical reefs in which the most suitable places to be exploited are shown, and the classification of different types of wave energy converters according to their construction features. It is also described in detail each of the stages that are part of the energy conversion. On the basis of the characteristics of tropical coral reefs, the paper puts forward a new type of raft wave energy device which can achieve high operational reliability and adaptability with cost-effective deployment

Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning

This paper comprehensively reviews the application of Artificial Intelligence (AI) in rehabilitation exercise assessment, with a particular focus on posture quality prediction. AI techniques, including Support Vector Machines (SVM), decision trees, random forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), show great potential in improving the accuracy and personalization of rehabilitation assessment. Various supervised and unsupervised learning methods are analyzed and their effectiveness in classifying rehabilitation movements and providing real-time feedback to improve rehabilitation outcomes is demonstrated. Despite some progress in the application of AI techniques in rehabilitation exercises, some challenges remain, especially in terms of model interpretability, generalizability to different patient populations, and handling differences in data distribution between clinical and home settings. Techniques such as Explainable Artificial Intelligence (XAI), transfer learning, and privacy-preserving machine learning can be a way to unlock the limitations of adopting AI techniques in a wider range of rehabilitation settings. This paper concludes by highlighting the need for more adaptable and interpretable AI systems that can be seamlessly integrated into different rehabilitation scenarios while maintaining patient data privacy and ethical standards